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Table 3 For each balancing (columns) and task (rows), we report the average AUPRCs (top table) and average AUROCs (bottom table) obtained by the two Bayesian classifiers (the average is computed over the four cell lines)

From: Boosting tissue-specific prediction of active cis-regulatory regions through deep learning and Bayesian optimization techniques

Task

Balanced

Full-balanced

Unbalanced

Wilcoxon

AUPRC

IE versus IP

0.627

0.787*

0.791*

0.251

AP versus IP

0.745

0.884*

0.901*

0.066

AE versus IE

0.660

0.885

0.814

 

AE versus AP

0.834

0.945

0.856

 

AE + AP versus else

0.671

0.882

0.824

 

All tasks

0.707

0.877

0.837

 

AUROC

IE versus IP

0.82*

0.819*

0.903

0.046

AP versus IP

0.919

0.931

0.960

 

AE versus IE

0.893*

0.921

0.9205*

0.052

AE versus AP

–

0.960*

0.956*

0.249

 

0.952*

–

0.956*

0.035

AE + AP versus else

0.929*

0.956

0.925*

0.066

All tasks

0.903

0.917

0.933

 
  1. Character * marks not statistically different pairs and, in this case, the last column reports the computed p value > 0.01. Bold text highlight the best performance, when this is statistically different from all the other values